Abstract
Multi-agent autonomous exploration is essential for applications such as environmental monitoring, search and rescue, and industrial-scale surveillance. However, effective coordination under communication constraints remains a significant challenge. Frontier exploration algorithms analyze the boundary between the known and unknown regions to determine the next-best view that maximizes exploratory gain. This article proposes an enhancement to existing frontier-based exploration algorithms by introducing a probabilistic approach to frontier prioritization. By leveraging Dirichlet process Gaussian mixture model (DP-GMM) and a probabilistic formulation of information gain, the method improves the quality of frontier prioritization. The proposed enhancement, integrated into two state-of-the-art multi-agent exploration algorithms, consistently improves performance across environments of varying clutter, communication constraints, and team sizes. Simulations showcase an average gain of $10\%$ and $14\%$ for the two algorithms across all combinations. Successful deployment in real-world experiments with a dual-drone system further corroborates these findings.
Abstract (translated)
多智能体自主探索对于环境监测、搜救和工业级监控等应用至关重要。然而，在通信约束条件下实现有效协调仍是一项重大挑战。前沿探索算法通过分析已知区域与未知区域之间的边界，来确定能最大化探索收益的下一最佳观测点。本文提出通过引入概率性方法对前沿进行优先级排序，以增强现有基于前沿的探索算法。该方法通过利用狄利克雷过程高斯混合模型（DP-GMM）和信息收益的概率性建模，提升了前沿优先级排序的质量。该改进被集成到两种最先进的多智能体探索算法中，在杂乱程度不同、通信约束各异以及团队规模变化的各类环境中均持续提升性能。仿真实验表明，两种算法在所有组合场景中平均分别提升10%和14%的性能。双无人机系统在真实世界实验中的成功部署进一步 corroborates 了这些发现。
URL
https://arxiv.org/abs/2604.03042
